Biology subjects

Taube, J. M.

Publications and source records attributed to Taube, J. M..

5 recordsLinked to original sources

bioRxiv2026

Interpretable spherical geometry of single-cell state transitions from dominant principal components

Single-cell RNA-seq atlases are commonly explored with nonlinear embeddings that preserve neighborhoods but provide limited coordinate-level interpretation. We asked whether projecting the dominant principal components (PCs) of single-cell gene expression onto a unit sphere would yield an interpretable coordinate system. SPHERE-PCA L2-normalizes the first three PC coordinates, aligns a biologically defined root to the north pole, and represents each cell by three coordinates: root-aligned geodesic distance ({theta}), angular position ({phi}), and pre-projection radial magnitude (r). Across developmental and disease-associated datasets, this representation reveals structured spherical geometry, ranging from near-great-circle trajectories to multi-arc manifolds. In developmental atlases, root-aligned geodesic distance increases as CytoTRACE-inferred stemness decreases, while gene-coordinate analyses separate programs associated with angular position from those associated with radial magnitude. Fixed-loading perturbations decompose each gene's effect on cell position into progression, branch- or state-position, and radial activity components. SPHERE-PCA therefore provides a deterministic, loading-preserving coordinate framework for interpreting dominant transcriptomic variance and establishing a transparent geometric coordinate framework for perturbation analysis and virtual-cell models.

systems biology↗

bioRxiv2025

Novel Predictive Spatial Biomarker in Non-Small Cell Lung Carcinoma: The Diversity of Niches Unlocking Treatment Sensitivity (DONUTS)

Probabilistic spatial modelling techniques developed on large-scale tumor-immune Atlases ([~]35M individually mapped cells; 50,000 high power fields) were used to characterize predictive features of treatment-responsive lung cancer. We identified CD8+FoxP3+ cell density as a robust pre-treatment biomarker for outcomes across disease stages and therapy types. In parallel, single-cell RNAseq studies of CD8+FoxP3+ T-cells revealed an activated, early effector phenotype, substantiating an anti-tumor role, and contrasting with CD4+FoxP3+ T-regulatory cells. A spatial biomarker was developed using an empirical probabilistic model to define the immediate cell neighbors or niche surrounding CD8+FoxP3+ cells and proximity to the tumor-stromal boundary. The resultant Diversity of Niches Unlocking Treatment Sensitivity (DONUTS) are more prevalent than the CD8+FoxP3+ cells themselves, mitigating sampling error in small biopsies. Further, the DONUTS only require four markers, are additive to PD-L1, and associate with tertiary lymphoid structure counts. Taken together, the DONUTS represent a next-generation predictive biomarker poised for clinical implementation. HIGHLIGHTSO_LILarge-scale tumor-immune Atlases drive robust computational biomarker development C_LIO_LICD8+FoxP3+ cells are anti-tumor T-cells and predict response to therapy C_LIO_LIThe niches or spatial donuts around CD8+FoxP3+ cells boost biomarker performance C_LIO_LICD8+FoxP3+ donuts are hallmarks of a larger immune organization that includes TLS C_LI

pathology↗

bioRxiv2022

Lung tumor-infiltrating Treg have divergent transcriptional profiles and function linked to checkpoint blockade response

Regulatory T cells (Treg) are conventionally viewed to suppress endogenous and therapyinduced anti-tumor immunity; however, their role in modulating responses to immune checkpoint blockade (ICB) is unclear. In this study, we integrated single-cell RNAseq/TCRseq of >73,000 tumor-infiltrating Treg (TIL-Treg) from anti-PD-1-treated and treatment naive non-small cell lung cancers (NSCLC) with single cell analysis of tumor-associated antigen (TAA)-specific Treg derived from a murine tumor model. We identified 10 subsets of human TIL-Treg, most of which have high concordance with murine TIL-Treg subsets. Notably, one subset selectively expresses high levels of OX40 and GITR, whose engangement by cognate ligand mediated proliferative programs and NF-kB activation, as well as multiple genes involved in Treg suppression, in particular LAG3. Functionally, the OX40hiGITRhi subset in the most highly suppressive ex vivo and Treg expression of OX40, GITR and LAG3, correlated with resistance to PD-1 blockade. Surprisingly, in the murine tumor model, we found that virtually all TIL-Treg expressing T cell receptors that are specific for TAA fully develop a distinct Th1-like signature over a two-week period after entry into the tumor, down-regulating FoxP3 and up-regulating expression of TBX21 (Tbet), IFN{gamma} and certain pro-inflammatory granzymes. Application of a gene score from the murine TAA-specific Th1-like Treg subset to the human single-cell dataset revealed a highly analogous subcluster that was enriched in anti-PD-1 responding tumors. These findings demonstrate that TIL-Treg partition into multiple distinct transcriptionally-defined subsets with potentially opposing effects on ICB-induced anti-tumor immunity and suggest that TAA-specific TIL-Treg may positively contribute to anti-tumor responses. One-Sentence SummaryWe define 10 subsets of lung cancer-infiltrating regulatory T cells, one of which is highly suppressive and enriched in anti-PD-1 non-responders and the other is Th1-like and is enriched in PD-1 responders.

immunology↗

bioRxiv2019

A new data-driven cell population discovery and annotation method for single-cell data, FAUST, reveals correlates of clinical response to cancer immunotherapy

High-dimensional single-cell cytometry is routinely used to characterize patient responses to cancer immunotherapy and other treatments. This has produced a wealth of datasets ripe for exploration but whose biological and technical heterogeneity make them difficult to analyze with current tools. We introduce a new interpretable machine learning method for single-cell mass and flow cytometry studies, FAUST, that robustly performs unbiased cell population discovery and annotation. FAUST processes data on a per-sample basis and returns biologically interpretable cell phenotypes that can be compared across studies, making it well-suited for the analysis and integration of complex datasets. We demonstrate how FAUST can be used for candidate biomarker discovery and validation by applying it to a flow cytometry dataset from a Merkel cell carcinoma anti-PD-1 trial and discover new CD4+ and CD8+ effector-memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. We then use FAUST to validate these correlates in an independent CyTOF dataset from a published metastatic melanoma trial. Importantly, existing state-of-the-art computational discovery approaches as well as prior manual analysis did not detect these or any other statistically significant T cell sub-populations associated with anti-PD-1 treatment in either data set. We further validate our methodology by using FAUST to replicate the discovery of a previously reported myeloid correlate in a different published melanoma trial, and validate the correlate by identifying it de novo in two additional independent trials. FAUSTs phenotypic annotations can be used to perform cross-study data integration in the presence of heterogeneous data and diverse immunophenotyping staining panels, enabling hypothesis-driven inference about cell sub-population abundance through a multivariate modeling framework we call Phenotypic and Functional Differential Abundance (PFDA). We demonstrate this approach on data from myeloid and T cell panels across multiple trials. Together, these results establish FAUST as a powerful and versatile new approach for unbiased discovery in single-cell cytometry.

bioinformatics↗

bioRxiv2019

Single cell RNA-seq in regenerative and fibrotic biomaterial environments defines new macrophage subsets

Macrophages play diverse roles in the immune response to infection, cancer, and wound healing where they respond to local environmental signals, yet identification and phenotypic characterization of functional subsets in vivo remains limited. We performed single cell RNA sequencing analysis on differentiated macrophages sorted from a biologic matrix-induced regenerative environment versus a synthetic biomaterial foreign body response (FBR), characterized by TH2/interleukin (IL)-4 and TH17/IL-17, respectively. In the regenerative environment, unbiased clustering and pseudotime analysis revealed distinct macrophage subsets responsible for antigen presentation, chemoattraction, and phagocytosis, as well as a small population with expression profiles of both dendritic cells and skeletal muscle. In the FBR environment, we identified a CD9hi+IL-36{gamma}+ macrophage subset that expressed TH17-associated molecules characteristic of certain auto-immune responses that were virtually absent in mice lacking the IL-17 receptor. Surface marker combinations including CD9 and CD301b defined macrophage fibrotic and regenerative subsets enabling functional assessment and identification in human tissue. Application of the terminal macrophage subsets to train the SingleCellNet algorithm and comparison to human and mouse macrophages in tumor, lung, and liver suggest broad relevance of macrophage classification. These distinct macrophage subsets demonstrate previously unrecognized myeloid phenotypes involved in different tissue responses and provide new targets for potential therapeutic modulation of certain pathologic states and tissue repair.

immunology↗